An efficient texture classification method is proposed that considers the effects of both the rotation and scale of texture images. In our method, the Gabor wavelets are adopted to extract local features of an image and the statistical properties of its graylevel intensities are used to represent the global features. Then, an adaptive, circular orientation normalization scheme is proposed to make the feature invariant to rotation, and an elastic crossfrequency searching mechanism is devised to reduce the effect of scaling. Our method is evaluated based on the Brodatz album and the Outex database, and the experimental results show that it outperforms the traditional algorithms.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Atomic and Molecular Physics, and Optics